I am using the observation with both leaf and flower observations
source("plot_temporal_spatial_northredoak.R")
temporal_spatial
rm(list = ls())
source("plot_data_map.R")
data_map
rm(list = ls())
source("table_spatial_temporal_statistics.R")
species_summary
plotly::ggplotly(species_summary)
path_npn <- "/nfs/turbo/seas-zhukai/phenology/NPN/individual_phenometrics/leaf_flower/with_climate/"
npn <- read_rds(stringr::str_c(path_npn, "Quercus", ".rds"))
npn %>%
group_by(dataset_id) %>%
summarise(count = n())
## # A tibble: 13 × 2
## dataset_id count
## <chr> <int>
## 1 '-9999' 25674
## 2 '-9999,16' 11
## 3 '-9999,3' 1810
## 4 '10' 25
## 5 '10,-9999' 1
## 6 '10,3' 2
## 7 '11' 51
## 8 '13' 273
## 9 '15' 73
## 10 '16' 7888
## 11 '16,-9999' 192
## 12 '3' 17252
## 13 '3,-9999' 1827
npn %>%
group_by(npn$partner_group) %>%
summarise(count = n()) %>%
arrange(desc(count))
## # A tibble: 186 × 2
## `npn$partner_group` count
## <chr> <int>
## 1 -9999 13842
## 2 National Ecological Observatory Network (NEON) 8112
## 3 Santa Monica Mountains NRA 4191
## 4 Great Smoky Mountains NP 2469
## 5 University at Buffalo 1630
## 6 New York Botanical Garden Forest Phenology 1586
## 7 Sedgwick Reserve 1562
## 8 John Muir NHS 1330
## 9 Meredith College 1058
## 10 Pepperwood Preserve 1053
## # ℹ 176 more rows